- Title
- Embedding-based neural network for investment return prediction
- Creator
- Zhu, Jianlong; Xian, Dan; Fengxiao,; Nie, Yichen
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190477
- Identifier
- vital:17648
- Identifier
-
https://doi.org/10.1109/CEI57409.2022.9950088
- Identifier
- ISBN:9781665476164 (ISBN)
- Abstract
- In addition to being familiar with policies, high investment returns also require extensive knowledge of relevant industry knowledge and news. In addition, it is necessary to leverage relevant theories for investment to make decisions, thereby amplifying investment returns. A effective investment return estimate can feedback the future rate of return of investment behavior. In recent years, deep learning are developing rapidly, and investment return prediction based on deep learning has become an emerging research topic. This paper proposes an embedding-based dual branch approach to predict an investment's return. This approach leverages embedding to encode the investment id into a low-dimensional dense vector, thereby mapping high-dimensional data to a low-dimensional manifold, so that high-dimensional features can be represented competitively. In addition, the dual branch model realizes the decoupling of features by separately encoding different information in the two branches. In addition, the swish activation function further improves the model performance. Our approach are validated on the Ubiquant Market Prediction dataset. The results demonstrate the superiority of our approach compared to Xgboost, Lightgbm and Catboost. © 2022 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022, Virtual online, 23-25 September 2022, 2022 2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) p. 670-673
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 IEEE
- Subject
- Dual branch; Embedding; Investment return prediction
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